sum(is.na(POLAND_ts))
library(forecast)
POLAND_ts <- tsclean(POLAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(POLAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
POLAND_ts_model <- auto.arima(POLAND_ts)
POLAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
POLAND_ts_forecast <- forecast (POLAND_ts_model, level=c(95), h=257)
plot(POLAND_ts_forecast)
POLAND_ts_forecast
write.table(POLAND_ts_forecast, file="Poland_TSA.csv", sep=",")
# Importing Data
SLOVAKIA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Slovakia.xlsx")
# Creating Time Series Data
SLOVAKIA_ts <- ts(SLOVAKIA, start=c(2004,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
SLOVAKIA_ts
sum(is.na(SLOVAKIA_ts))
library(forecast)
SLOVAKIA_ts <- tsclean(SLOVAKIA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SLOVAKIA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SLOVAKIA_ts_model <- auto.arima(SLOVAKIA_ts)
SLOVAKIA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SLOVAKIA_ts_forecast <- forecast (SLOVAKIA_ts_model, level=c(95), h=258)
plot(SLOVAKIA_ts_forecast)
SLOVAKIA_ts_forecast
write.table(SLOVAKIA_ts_forecast, file="Slovakia_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SWEDEN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Sweden.xlsx")
# Creating Time Series Data
SWEDEN_ts <- ts(SWEDEN, start=c(2004,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
SWEDEN_ts
sum(is.na(SWEDEN_ts))
library(forecast)
SWEDEN_ts <- tsclean(SWEDEN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SWEDEN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SWEDEN_ts_model <- auto.arima(SWEDEN_ts)
SWEDEN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SWEDEN_ts_forecast <- forecast (SWEDEN_ts_model, level=c(95), h=258)
plot(SWEDEN_ts_forecast)
SWEDEN_ts_forecast
write.table(SWEDEN_ts_forecast, file="Sweden_TSA.csv", sep=",")
# Importing Data
THAILAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Thailand.xlsx")
# Creating Time Series Data
THAILAND_ts <- ts(THAILAND, start=c(2007,02), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
THAILAND_ts
sum(is.na(THAILAND_ts))
library(forecast)
THAILAND_ts <- tsclean(THAILAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(THAILAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
THAILAND_ts_model <- auto.arima(THAILAND_ts)
THAILAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
THAILAND_ts_forecast <- forecast (THAILAND_ts_model, level=c(95), h=257)
plot(THAILAND_ts_forecast)
THAILAND_ts_forecast
write.table(THAILAND_ts_forecast, file="Thailand_TSA.csv", sep=",")
# Importing Data
USA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/USA.xlsx")
# Creating Time Series Data
USA_ts <- ts(USA, start=c(2005,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
USA_ts
sum(is.na(USA_ts))
library(forecast)
USA_ts <- tsclean(USA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(USA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
USA_ts_model <- auto.arima(USA_ts)
USA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
USA_ts_forecast <- forecast (USA_ts_model, level=c(95), h=257)
plot(USA_ts_forecast)
USA_ts_forecast
write.table(USA_ts_forecast, file="USA_TSA.csv", sep=",")
# Importing Data
UK <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/UK.xlsx")
# Creating Time Series Data
UK_ts <- ts(UK, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
UK_ts
sum(is.na(UK_ts))
library(forecast)
UK_ts <- tsclean(UK_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(UK_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
UK_ts_model <- auto.arima(UK_ts)
UK_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
UK_ts_forecast <- forecast (UK_ts_model, level=c(95), h=257)
plot(UK_ts_forecast)
UK_ts_forecast
write.table(UK_ts_forecast, file="UK_TSA.csv", sep=",")
library(readxl)
France <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
View(France)
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=257)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
write.table(FRANCE_ts_forecast, file="France_TSA.csv", sep=",")
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=258)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
write.table(FRANCE_ts_forecast, file="France_TSA.csv", sep=",")
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=258)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,10), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,08), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Checking the Imported Data
View(FRANCE)
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,07), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=266)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,05), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=271)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,05), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=271)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,11), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=265)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2018,06), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=270)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
write.table(FRANCE_ts_forecast, file="France_TSA.csv", sep=",")
# Importing Data
NEITHERLAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Neitherland.xlsx")
# Creating Time Series Data
NEITHERLAND_ts <- ts(NEITHERLAND, start=c(2004,03), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
NEITHERLAND_ts
sum(is.na(NEITHERLAND_ts))
library(forecast)
NEITHERLAND_ts <- tsclean(NEITHERLAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(NEITHERLAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
NEITHERLAND_ts_model <- auto.arima(NEITHERLAND_ts)
NEITHERLAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
NEITHERLAND_ts_forecast <- forecast (NEITHERLAND_ts_model, level=c(95), h=258)
plot(NEITHERLAND_ts_forecast)
NEITHERLAND_ts_forecast
# Importing Data
NEITHERLAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Neitherland.xlsx")
# Viewing and Checking the Created Time Series Data
NEITHERLAND_ts
# Creating Time Series Data
NEITHERLAND_ts <- ts(NEITHERLAND, start=c(2004,03), end=c(2018,06), frequency=12)
# Viewing and Checking the Created Time Series Data
NEITHERLAND_ts
sum(is.na(NEITHERLAND_ts))
library(forecast)
NEITHERLAND_ts <- tsclean(NEITHERLAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(NEITHERLAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
NEITHERLAND_ts_model <- auto.arima(NEITHERLAND_ts)
NEITHERLAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
NEITHERLAND_ts_forecast <- forecast (NEITHERLAND_ts_model, level=c(95), h=270)
plot(NEITHERLAND_ts_forecast)
NEITHERLAND_ts_forecast
write.table(NEITHERLAND_ts_forecast, file="Neitherland_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
options
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Turkey/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2018,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=269)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
